Weed Detection for Selective Spraying: a Review

Liu, Bo; Bruch, Ryan

doi:10.1007/s43154-020-00001-w

Cited by 123 publications

(74 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Precision weed control such as selective spraying or accurate mechanical removal of weeds, is a challenging task that aims to reduce the amount of herbicides without compromising the quality of crops [5]. Accurate weed detection in croplands, as a prerequisite for applying any precision weed management technology [6], is still a challenging step toward the development of site-specific weed control machines, especially when there are intra-row weeds that are highly overlapped with the main plant. Efficient weed removal weather using variable-rate sprayers, or precise mechanical, electrical, or thermal hoeing systems, preliminary requires to detect and segregate weeds from main crop [7].…”

Section: Introductionmentioning

confidence: 99%

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Bakhshipour

2021

IEEE Access

View full text Add to dashboard Cite

Crop and weeds identification is of important steps towards the development of efficient automotive weed control systems. The higher the accuracy of plant detection and classification, the higher the performance of the weeding machine. In this study, the capability of two popular boosting methods including Adaboost.M1 and LogitBoost algorithms was evaluated to enhance the plant classification performance of four classifiers, namely Multi-Layer Perceptron (MLP), k-Nearest Neighbors (kNN), Random Forest (RF), and Support Vector Machine (SVM). Four feature filtering techniques including Correlationbased Feature Selection (CFS), Information Gain (IG), Gain Ratio (GR), and OneR were applied to the imageextracted features and 10 of the most significant features were selected and fed into single and boosted classifiers. The RF model trained by IG selected features (IG-RF) was the most appropriate classifier among the evaluated models whether in single or boosted modes. It was also found that boosting of IG-RF by using Adaboost.M1 and LogitBoost algorithms improved the classification accuracy. Regarding the performance values, the LogitBoost-IG-RF structure, which provided a classification accuracy of 99.58%, a kappa (k) of 0.9948, and a Root Mean Squared Error (RMSE) of 0.0688 on training dataset, was selected as the most appropriate classifier for plant discrimination in peanut fields. The accuracy, k, and RMSE criteria of this combination on test dataset were 95.00%, 0.9375, and 0.1591, respectively. It was concluded that combination of boosting algorithms and feature selection methods can promote plant type discrimination accuracy, which is a crucial factor in the development of precision weed control systems.

show abstract

Section: Introductionmentioning

confidence: 99%

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Bakhshipour

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Nevertheless, our non-transgenic glyphosate-tolerant wheat is resistant to commercial levels of glyphosate herbicide. Non-transgenic glyphosate tolerant wheat may have uses in weed and disease control (Anderson and Kolmer 2005) (Feng et al 2005) and this wheat may benefit from advancements in robotic weeding technology that enables targeted herbicide spraying (Liu and Bruch 2020). In addition, the glyphosate tolerance may, in the future, be further improved by adding additional alleles in genes that generate glyphosate tolerance by independent mechanisms-e.g.…”

Section: Discussionmentioning

confidence: 99%

Development of non-transgenic glyphosate tolerant wheat by TILLING

Moehs

Austill

Facciotti

et al. 2020

Preprint

View full text Add to dashboard Cite

Glyphosate (N-phosphonomethyl-glycine) is the world’s most widely used broad spectrum, post-emergence herbicide. It inhibits the chloroplast-targeted enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS; EC 2.5.1.19), a component of the plant and microorganism-specific shikimate pathway and a key catalyst in the production of aromatic amino acids. Variants of EPSPS that are not inhibited by glyphosate due to particular amino acid alterations in the active site of the enzyme are known. Some of these variants have been identified in weed species that have developed resistance to glyphosate because of the strong selective pressure of continuous, heavy glyphosate use. We have used TILLING (Targeting Induced Local Lesions in Genomes), a non-transgenic, target-selected, reverse genetics mutation breeding technique, and conventional genetic crosses, to identify and combine, through two rounds of mutagenesis, wheat lines having both T102I and P106S (so-called TIPS enzyme) mutations in both the A and the D sub-genome homoeologous copies of the wheat EPSPS gene. The combined effects of the T102I and P106S mutations are known from previous work in multiple species to minimize the binding of the herbicide while maintaining the affinity of the catalytic site for its native substrates. These novel wheat lines exhibit substantial tolerance to commercially relevant levels of glyphosate.

show abstract

“…Weed management is critical for agricultural production. Weeds compete with crops for resources (e.g., light, water, and nutrients) and introduce diseases that can cause yield loss (Liu & Bruch, 2020;Machado, 2007;Monaco et al, 1981). Weeds are unevenly distributed as patches across fields (Cardina et al, 1995;Dieleman & Mortensen, 1999;Gerhards et al, 1997;Johnson et al, 1996); however, conventional weed management relies on whole-field management (Ghanizadeh & Harrington, 2019;Llewellyn et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Discriminating crops/weeds in an upland rice field from UAV images with the SLIC-RF algorithm

Kawamura

Asai

Yasuda

et al. 2020

Plant Production Science

View full text Add to dashboard Cite

In this study, we propose a method for discriminating crops/weeds in upland rice fields using a commercial unmanned aerial vehicles (UAVs) and red-green-blue (RGB) cameras with the simple linear iterative clustering (SLIC) algorithm and random forest (RF) classifier. In the SLIC-RF algorithm, we evaluated different combinations of input features: three color spaces (RGB, hue-saturation-brightness [HSV], CIE-L*a*b), canopy height model (CHM), spatial texture (Texture) and four vegetation indices (VIs) (excess green [ExG], excess red [ExR], green-red vegetation index [GRVI] and color index of vegetation extraction [CIVE]). Among the color spaces, the HSV-based SLIC-RF model showed the best performance with the highest out-of-bag (OOB) accuracy (0.904). The classification accuracy was improved by the combination of HSV with CHM, Texture, ExG, or CIVE. The highest OOB accuracy (0.915) was obtained from the HSV+Texture combination. The greatest errors from the confusion matrix occurred in the classification between crops and weeds, while soil could be classified with a very high accuracy. These results suggest that with the SLIC-RF algorithm developed in this study, rice and weeds can be discriminated by consumer-grade UAV images with acceptable accuracy to meet the needs of site-specific weed management (SSWM) even in the early growth stages of small rice plants..

show abstract

Weed Detection for Selective Spraying: a Review

Cited by 123 publications

References 40 publications

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Development of non-transgenic glyphosate tolerant wheat by TILLING

Discriminating crops/weeds in an upland rice field from UAV images with the SLIC-RF algorithm

Contact Info

Product

Resources

About